Graphs and networks are prominent tools to model various kinds of data in almost every branch of science. Due to this, graph classification problems also have a crucial role in a wide range of applications from biology to social science. In many of these applications, the success of algorithms is often attributed to recognizing the presence or absence of specific substructures, e.g. atomic groups in case of molecule and protein functions, or cliques in case of social networks [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
This suggests that some parts of the graph are “more important” than others, and hence it is an essential aspect of any successful classification algorithm to find and focus on these parts.
